• Alaska's ice roads and investment decision in drilling: an empirical analysis

      Azmi Wendler, Sarah; Baek, Jungho; Reynolds, Douglas B.; Herrmann, Mark (2019-08)
      This thesis applies Autoregressive Distributed Lag modeling techniques to estimate the effects of ice road season lengths on exploration activities in Alaska within the North Slope. This analysis uses data on winter off-road travel from 2001-2018 in monthly intervals against exploration wells spudded. It is found that while ice roads do not affect overall drilling activities in the North Slope, the lengths of the season plays significant part in exploration of new fields. While this subject has become a popular subject due to variations in the ice road season, no similar statistical analysis has been conducted to date. Oil prices, production and Alaska's oil policy were also found to be important variables in characterizing exploration activity.
    • An exploration of own and cross-price elasticity of demand for residential heating in the Fairbanks North Star Borough

      Graham, Noelle J.; Little, Joseph; Baek, Jungho; Kennedy, Camilla (2019-05)
      The purpose of this study is to utilize community level household energy consumption data to determine the short-run own- and cross-price elasticity of heating oil and wood using the proportionally calibrated almost idea demand system model. Elasticity values can identify how residents of the Fairbanks North Star Borough will potentially alter home heating practices in response to a change in home heating oil price. Results indicate that values for own-price elasticity for oil is -0.259, with a 95% confidence interval of [-0.272, -0.246]. Based on predicted values a 1% increase in the price of heating oil is estimated to result in a reduction of 0.259% in the quantity of residential heating oil consumed by the average household. Cross-price elasticity estimates of wood with respect to a change in the price of oil is 0.198 with a 95% confidence interval of [0.171, 0.234]. Based on predicted values, a 1% increase in the price of oil is predicted to increase wood consumption by 0.198%. In addition, this study utilized a Monte Carlo Simulation with estimated elasticity parameters to predict the change in household level energy consumption of wood and heating oil given an increase in heating oil prices. Approximately 71% of households are predicted to decrease overall energy consumption. 83.5% of households are predicted to decrease oil consumption, and 57.3% of houses are predicted to increase wood consumption. Through evaluating household's energy consumption decisions in the face of changing prices, these results can inform effective air quality policies.